


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=13   ,    snake_case=3   ,    snake_case=True   ,    snake_case=True   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=224   ,    snake_case=1000   ,    snake_case=[3, 3, 6, 4]   ,    snake_case=[48, 56, 112, 220]   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							parent
						UpperCamelCase__       =							batch_size
						UpperCamelCase__       =							num_channels
						UpperCamelCase__       =							is_training
						UpperCamelCase__       =							use_labels
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							num_labels
						UpperCamelCase__       =							image_size
						UpperCamelCase__       =							layer_depths
						UpperCamelCase__       =							embed_dims




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						UpperCamelCase__       =							None
						if self.use_labels:
									UpperCamelCase__       =							ids_tensor([self.batch_size]   ,    self.num_labels  )

						UpperCamelCase__       =							self.get_config()

						return config, pixel_values, labels




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return SwiftFormerConfig(
						    depths=self.layer_depths   ,    embed_dims=self.embed_dims   ,    mlp_ratio=4   ,    downsamples=[True, True, True, True]   ,    hidden_act="gelu"   ,    num_labels=self.num_labels   ,    down_patch_size=3   ,    down_stride=2   ,    down_pad=1   ,    drop_rate=0.0   ,    drop_path_rate=0.0   ,    use_layer_scale=snake_case   ,    layer_scale_init_value=1E-5   ,    )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							SwiftFormerModel(config=snake_case  )
						model.to(snake_case  )
						model.eval()
						UpperCamelCase__       =							model(snake_case  )
						self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.embed_dims[-1], 7, 7)  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.num_labels
						UpperCamelCase__       =							SwiftFormerForImageClassification(snake_case  )
						model.to(snake_case  )
						model.eval()
						UpperCamelCase__       =							model(snake_case   ,    labels=snake_case  )
						self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)  )

						UpperCamelCase__       =							SwiftFormerForImageClassification(snake_case  )
						model.to(snake_case  )
						model.eval()

						UpperCamelCase__       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
						UpperCamelCase__       =							model(snake_case  )
						self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						((UpperCamelCase__),   (UpperCamelCase__),   (UpperCamelCase__))       =							self.prepare_config_and_inputs()
						UpperCamelCase__       =							{"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class 							lowerCamelCase__						(				UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Tuple 			=		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
			_UpperCamelCase		:       Optional[int] 			=		(
			    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:       Any 			=		False
			_UpperCamelCase		:       int 			=		False
			_UpperCamelCase		:       Tuple 			=		False
			_UpperCamelCase		:       str 			=		False
			_UpperCamelCase		:       List[Any] 			=		False




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							SwiftFormerModelTester(self  )
						UpperCamelCase__       =							ConfigTester(
						    self   ,    config_class=snake_case   ,    has_text_modality=snake_case   ,    hidden_size=37   ,    num_attention_heads=12   ,    num_hidden_layers=12   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			@unittest.skip(reason="SwiftFormer does not use inputs_embeds"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__       =							model_class(snake_case  )
									UpperCamelCase__       =							model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case   ,    nn.Linear  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__       =							model_class(snake_case  )
									UpperCamelCase__       =							inspect.signature(model.forward  )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCamelCase__       =							[*signature.parameters.keys()]

									UpperCamelCase__       =							["pixel_values"]
									self.assertListEqual(arg_names[:1]   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case  )




			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__       =							SwiftFormerModel.from_pretrained(snake_case  )
									self.assertIsNotNone(snake_case  )




			@unittest.skip(reason="SwiftFormer does not output attentions"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case   ,    snake_case   ,    snake_case  ):
									UpperCamelCase__       =							model_class(snake_case  )
									model.to(snake_case  )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__       =							model(**self._prepare_for_class(snake_case   ,    snake_case  )  )

									UpperCamelCase__       =							outputs.hidden_states

									UpperCamelCase__       =							8
									self.assertEqual(len(snake_case  )   ,    snake_case  )  # TODO

									# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
									# with the width and height being successively divided by 2, after every 2 blocks
									for i in range(len(snake_case  )  ):
												self.assertEqual(
												    hidden_states[i].shape   ,    torch.Size(
												        [
												            self.model_tester.batch_size,
												            self.model_tester.embed_dims[i // 2],
												            (self.model_tester.image_size // 4) // 2 ** (i // 2),
												            (self.model_tester.image_size // 4) // 2 ** (i // 2),
												        ]  )   ,    )

						UpperCamelCase__,   UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__       =							True
									check_hidden_states_output(snake_case   ,    snake_case   ,    snake_case  )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__       =							True

									check_hidden_states_output(snake_case   ,    snake_case   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						def _config_zero_init(snake_case  ):
									UpperCamelCase__       =							copy.deepcopy(snake_case  )
									for key in configs_no_init.__dict__.keys():
												if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
															setattr(snake_case   ,    snake_case   ,    1E-1_0  )
												if isinstance(getattr(snake_case   ,    snake_case   ,    snake_case  )   ,    snake_case  ):
															UpperCamelCase__       =							_config_zero_init(getattr(snake_case   ,    snake_case  )  )
															setattr(snake_case   ,    snake_case   ,    snake_case  )
									return configs_no_init

						UpperCamelCase__,   UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs_for_common()

						UpperCamelCase__       =							_config_zero_init(snake_case  )
						for model_class in self.all_model_classes:
									UpperCamelCase__       =							model_class(config=snake_case  )
									for name, param in model.named_parameters():
												if param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1E9) / 1E9).round().item()   ,    [0.0, 1.0]   ,    msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''   ,    )







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass



def 							UpperCamelCase_(					)->		List[Any]:
			UpperCamelCase__       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
			return image







@require_torch
@require_vision
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"  ) if is_vision_available() else None







			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"  ).to(snake_case  )

						UpperCamelCase__       =							self.default_image_processor
						UpperCamelCase__       =							prepare_img()
						UpperCamelCase__       =							image_processor(images=snake_case   ,    return_tensors="pt"  ).to(snake_case  )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__       =							model(**snake_case  )

						# verify the logits
						UpperCamelCase__       =							torch.Size((1, 1000)  )
						self.assertEqual(outputs.logits.shape   ,    snake_case  )

						UpperCamelCase__       =							torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]  ).to(snake_case  )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,    snake_case   ,    atol=1E-4  )  )






def 							UpperCamelCase_(					_A :list[list]	)->		list[list]:
			UpperCamelCase__       =							current_set.copy()
			for row_index, row in enumerate(_A	):
						UpperCamelCase__       =							row[0]
						for column_index, column in enumerate(_A	):
									if magnitude == 0:
												UpperCamelCase__       =							column
												continue
									UpperCamelCase__       =							column / magnitude
    # Subtract to cancel term
			UpperCamelCase__       =							current_set[0]
			UpperCamelCase__       =							[first_row]
			UpperCamelCase__       =							current_set[1::]
			for row in current_set:
						UpperCamelCase__       =							[]
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(_A	)
									continue
						for column_index in range(len(_A	)	):
									temp_row.append(first_row[column_index] - row[column_index]	)
						final_set.append(_A	)
			# Create next recursion iteration set
			if len(final_set[0]	) != 3:
						UpperCamelCase__       =							final_set[0]
						UpperCamelCase__       =							[]
						UpperCamelCase__       =							[]
						for row in final_set[1::]:
									current_first_column.append(row[0]	)
									next_iteration.append(row[1::]	)
						UpperCamelCase__       =							simplify(_A	)
						for i in range(len(_A	)	):
									resultant[i].insert(0					,  current_first_column[i]	)
						resultant.insert(0					,  _A	)
						UpperCamelCase__       =							resultant
			return final_set
def 							UpperCamelCase_(					_A :list[list]	)->		list:
			if len(_A	) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
			UpperCamelCase__       =							len(_A	) + 1
			if any(len(_A	) != _length for item in equations	):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
			for row in equations:
						if any(not isinstance(_A					,  (int, float)	) for column in row	):
									raise ValueError("solve_simultaneous() requires lists of integers"	)
			if len(_A	) == 1:
						return [equations[0][-1] / equations[0][0]]
			UpperCamelCase__       =							equations.copy()
			if any(0 in row for row in data_set	):
						UpperCamelCase__       =							data_set.copy()
						UpperCamelCase__       =							[]
						for row_index, row in enumerate(_A	):
									if 0 not in row:
												UpperCamelCase__       =							data_set.pop(_A	)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"	)
						data_set.insert(0					,  _A	)
			UpperCamelCase__       =							data_set.copy()
			UpperCamelCase__       =							simplify(_A	)
			UpperCamelCase__       =							simplified[::-1]
			UpperCamelCase__       =							[]
			for row in simplified:
						UpperCamelCase__       =							row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0	)
												continue
									solutions.append(current_solution / row[-2]	)
									continue
						UpperCamelCase__       =							row.copy()[: len(_A	) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0	)
						if len(_A	) == 0:
									solutions.append(0	)
									continue
						UpperCamelCase__       =							temp_row[1::]
						UpperCamelCase__       =							temp_row[::-1]
						for column_index, column in enumerate(_A	):
									current_solution -= column * solutions[column_index]
						solutions.append(_A	)
			UpperCamelCase__       =							[]
			for item in solutions:
						final.append(float(round(_A					,  5	)	)	)
			return final[::-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__UpperCamelCase     		=							[
						    [2, 1, 1, 1, 1, 4],
						    [1, 2, 1, 1, 1, 5],
						    [1, 1, 2, 1, 1, 6],
						    [1, 1, 1, 2, 1, 7],
						    [1, 1, 1, 1, 2, 8],
						]
						print(solve_simultaneous(eq))
						print(solve_simultaneous([[4, 2]]))


